This article proposes a partial solution to these three issues, in the form of controlled generation of synthetic text with large language models. I provide a conceptual overview of text generation, guidance on when researchers should prefer different techniques for generating synthetic text, a discussion of ethics, and a simple technique for improving the quality of synthetic text. I demonstrate the usefulness of synthetic text with three applications: generating synthetic tweets describing the fighting in Ukraine, synthetic news articles describing specified political events for training an event detection system, and a multilingual corpus of populist manifesto statements for training a sentence-level populism classifier.

Artificial Intelligence (AI) has made incredible progress recently. On the one hand, advanced foundation models like ChatGPT can offer powerful conversation, in-context learning and code generation abilities on a broad range of open-domain tasks. They can also generate high-level solution outlines for domain-specific tasks based on the common sense knowledge they have acquired. However, they still face difficulties with some specialized tasks because they lack enough domain-specific data during pre-training or they often have errors in their neural network computations on those tasks that need accurate executions. On the other hand, there are also many existing models and systems (symbolic-based or neural-based) that can do some domain-specific tasks very well. However, due to the different implementation or working mechanisms, they are not easily accessible or compatible with foundation models. Therefore, there is a clear and pressing need for a mechanism that can leverage foundation models to propose task solution outlines and then automatically match some of the sub-tasks in the outlines to the off-the-shelf models and systems with special functionalities to complete them. Inspired by this, we introduce TaskMatrix.AI as a new AI ecosystem that connects foundation models with millions of APIs for task completion. Unlike most previous work that aimed to improve a single AI model, TaskMatrix.AI focuses more on using existing foundation models (as a brain-like central system) and APIs of other AI models and systems (as sub-task solvers) to achieve diversified tasks in both digital and physical domains. As a position paper, we will present our vision of how to build such an ecosystem, explain each key component, and use study cases to illustrate both the feasibility of this vision and the main challenges we need to address next.

Public opinion reflects and shapes societal behavior, but the traditional survey-based tools to measure it are limited. We introduce a novel approach to probe media diet models -- language models adapted to online news, TV broadcast, or radio show content -- that can emulate the opinions of subpopulations that have consumed a set of media. To validate this method, we use as ground truth the opinions expressed in U.S. nationally representative surveys on COVID-19 and consumer confidence. Our studies indicate that this approach is (1) predictive of human judgements found in survey response distributions and robust to phrasing and channels of media exposure, (2) more accurate at modeling people who follow media more closely, and (3) aligned with literature on which types of opinions are affected by media consumption. Probing language models provides a powerful new method for investigating media effects, has practical applications in supplementing polls and forecasting public opinion, and suggests a need for further study of the surprising fidelity with which neural language models can predict human responses.

There is growing interest in designing recommender systems that aim at being fair towards item producers or their least satisfied users. Inspired by the domain of inequality measurement in economics, this paper explores the use of generalized Gini welfare functions (GGFs) as a means to specify the normative criterion that recommender systems should optimize for. GGFs weight individuals depending on their ranks in the population, giving more weight to worse-off individuals to promote equality. Depending on these weights, GGFs minimize the Gini index of item exposure to promote equality between items, or focus on the performance on specific quantiles of least satisfied users. GGFs for ranking are challenging to optimize because they are non-differentiable. We resolve this challenge by leveraging tools from non-smooth optimization and projection operators used in differentiable sorting. We present experiments using real datasets with up to 15k users and items, which show that our approach obtains better trade-offs than the baselines on a variety of recommendation tasks and fairness criteria.

FamilySearch has made more than 2.6 billion historical resources available to the public, and according to John Alexander who is a senior product manager there, there's a lot more on the way. More than 5 billion more documents -- collected and converted to digital images -- need to be transcribed to make them searchable and usable in FamilySearch's database. And 1 to 2 million more are added every single day. With the development of new artificial intelligence technology, there's more hope of getting billions of records to families looking for information about their relatives in as little as five years. And it's already being tested and used.

As generative AI and its eerily human chatbots explode into the public realm -- including Google's Bard, released yesterday -- Silicon Valley looks ripe for another big era of disruption. Think about the era of personal computers, or online businesses, or social platforms, when an accessible, unpredictable new idea shakes up the establishment. But unlike earlier disruptions, the reality of the generative AI race is already looking a little … top-heavy. With AI, the big innovation isn't the kind of cheap, accessible technology that helps garage startups grow into world-changing new companies. The models that underpin the AI era can be extremely, extremely expensive to build.

If you believe in the multibillion-dollar valuations, the prognostications from some of tech's most notable figures, and the simple magic of getting a computer to do your job for you, then you might say we're at the start of the chatbot era. Last November, OpenAI released ChatGPT into the unsuspecting world: It became the fastest-growing consumer app in history and immediately seemed to reconfigure how people think of conversational programs. Chatbots have existed for decades, but they haven't seemed especially intelligent--nothing like the poetry-writing, email-summarizing machines that have sprouted up recently. OpenAI has defined the moment, but there are plenty of competitors, including major players such as Google and Meta and lesser-known start-ups such as Anthropic. This cheat sheet tracks some of the most notable chatbot contenders through a few metrics: Can you actually use them? Do they contain glaring flaws?

Recent advancements in the field of Artificial Intelligence (AI) have taken the world by storm. Deepfakes, DALL-E 2, ChatGPT, and all of their sister concerns have prepared the path for several arguments about the ethics, prospects, and eventual future of AI. Now, by using mid-journey AI, a Facebook user named Razib Jahan Ferdous rendered some pictures of the top Hollywood celebrities having iftar. In a Facebook post, he shared twelve pictures with the caption, "After the Eid shopping, in an iftar invitation at Sultan's Dine." In the picture, we can see actors like Morgan Freeman, Leonardo DiCaprio, Will Smith, Robert Downey Jr, Christian Bale, Tom Hanks, Tom Cruise, Brad Pitt, Dwayne Johnson, Arnold Schwarzenegger, Chris Hemsworth, and Angelina Jolie having iftar like us in a very Islamic way.

Since the emergence of OpenAI's ChatGPT - an artificial intelligence-powered chatbot, people are worried that the powerful technology may eliminate several jobs in the future. Recently, Sam Altman, the CEO of the company that created ChatGPT, also revealed that he was "a little bit scared" of his company's invention. Now, a new study by OpenAI, Open Research, and the University of Pennsylvania has revealed the jobs that are most at risk of being lost due to the technological revolution triggered by ChatGPT, Metro reported. The study is titled "GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models'' which basically identifies the potential exposure that each job has to large language models. According to the study, higher-paying jobs are more likely to be affected compared to lower-paying ones. Jobs that don't require formal educational credentials are safe from ChatGPT while professions that require proficiency in programming and writing are more susceptible to being automated. Jobs that are heavily reliant on scientific and critical thinking skills are less prone to automation. Meanwhile, people with professional degrees and higher incomes are more at risk of losing their jobs to AI. Sectors such as Finance, Education, Journalism, Engineering, and Graphic Design face a greater threat of being supplemented by AI. OpenAI recently launched GPT-4, the AI technology that exhibits human-level performance on some professional and academic tasks. According to the company blog, the latest chatbot is "more creative and collaborative than ever before" and would "solve difficult problems with greater accuracy" than its earlier versions. During an interview with ABC News, OpenAI CEO Sam Altman spoke about ChatGPT and said, "It is going to eliminate a lot of current jobs, that's true.

Hosted by Dylan Doyle-Burke and Jessie J Smith, Radical AI is a podcast featuring the voices of the future in the field of artificial intelligence ethics. In this episode, Dylan and Jess discuss Meredith Broussard's influential new book, "More than a Glitch: Confronting Race, Gender, and Ability Bias in Tech". In this episode, we discuss Meredith Broussards influential new book, "More than a Glitch: Confronting Race, Gender, and Ability Bias in Tech" – published by MIT Press. Meredith is a data journalist, an associate professor at the Arthur L. Carter Journalism Institute of New York University, a research director at the NYU Alliance for Public Interest Technology, and the author of several books, including "More Than a Glitch" (which we cover in this episode) and "Artificial Unintelligence: How Computers Misunderstand the World." Her academic research focuses on artificial intelligence in investigative reporting and ethical AI, with a particular interest in using data analysis for social good.